Topic
Naturalness
About: Naturalness is a research topic. Over the lifetime, 1305 publications have been published within this topic receiving 31737 citations.
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TL;DR: In this article , underwater image enhancement using Laplace decomposition has been presented, in contrast, color, object prominence, edge preservation, reduced artifacts, and naturalness as compared to other methods related to underwater image enhancing.
Abstract: This letter presents underwater image enhancement using Laplace decomposition. Underwater image undergoes Laplace decomposition resulting in low- and high-frequency bands. Haze is removed from the low-frequency band, and then it is normalized for white balancing. The high-frequency band is amplified for edge preservation. Adding the two frequency images results in an enhanced image. It has improved, in contrast, color, object prominence, edge preservation, reduced artifacts, and naturalness as compared to other methods related to underwater image enhancement.
4 citations
07 Jul 2008
TL;DR: A frequency estimation method using a random walk to keep the naturalness of the sound without using a separate noise model and provides large flexibility for the user and reduces the number of synthesis parameters compared to traditional analysis/re-synthesis methods.
Abstract: We introduce a reduced parameter synthesis model for the spectral synthesis of musical sounds, which preserves the timbre and the naturalness of the musical sound. It also provides large flexibility for the user and reduces the number of synthesis parameters compared to traditional analysis/re-synthesis methods. The proposed model is almost completely independent from a previous spectral analysis. We present a frequency estimation method using a random walk to keep the naturalness of the sound without using a separate noise model. Three different approaches have been tested to estimate the amplitude values for the synthesis, namely, local optimization, the use of a lowpass filter and polynomial fitting. All of these approaches give good results, especially for the sustain part of the signal.
4 citations
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TL;DR: In this article, the authors discuss the epistemic attitudes of particle physicists on the discovery of the Higgs boson at the Large Hadron Collider (LHC), based on questionnaires and interviews made shortly before and shortly after the discovery in 2012.
Abstract: Our paper discusses the epistemic attitudes of particle physicists on the discovery of the Higgs boson at the Large Hadron Collider (LHC). It is based on questionnaires and interviews made shortly before and shortly after the discovery in 2012. We show, to begin with, that the discovery of a Standard Model (SM) Higgs boson was less expected than is sometimes assumed. Once the new particle was shown to have properties consistent with SM expectations - albeit with significant experimental uncertainties -, there was a broad agreement that 'a' Higgs boson had been found. Physicists adopted a twopronged strategy. On the one hand, they treated the particle as a SM Higgs boson and tried to establish its properties with higher precision; on the other hand, they searched for any hints of physics beyond the SM. This motivates our first philosophical thesis: the Higgs discovery, being of fundamental importance and establishing a new kind of particle, represented a crucial experiment if one interprets this notion in an appropriate sense. By embedding the LHC into thetradition of previous precision experiments and the experimental strategies thus established, Duhemian underdetermination is kept at bay. Second, our case study suggests that criteria of theory (or model) preference should be understood as epistemic and pragmatic values that have to be weighed in factual research practice. The Higgs discovery led to a shift from pragmatic to epistemic values as regards the mechanisms of electroweak symmetry breaking. Complex criteria, such as naturalness, combine epistemic and pragmatic values, but are coherently applied by the community.
4 citations
TL;DR: Experimental results demonstrate that the proposed unsupervised network is able to recover natural structure and color images more effectively, which would also help to enlarge the practical application without collecting paired datasets in advance.
Abstract: A novel structure-aware unsupervised network is proposed to deal with low-light image enhancement issues based on the inspiration of Retinex theory and self-supervised perceptual loss. It comprises four main components, namely the original structural similarity module, the novel color consistency module, the attentional enhancement module, and the naturalness discriminator module. Specially for the structural similarity module, an embedded structural feature extractor (SFE) model capable of generating structure correspondence is well designed and pre-trained by employing the contrastive learning technique, and a multi-scale structural similarity distance is introduced to optimize the SFE network. Besides, a self-supervised color consistency module is established by using a degraded estimation algorithm for recovering the missing colors. The whole enhancement framework operates in unsupervised manners and finally obtains the best naturalness image quality evaluator metric. Experimental results demonstrate that the proposed unsupervised network is able to recover natural structure and color images more effectively, which would also help to enlarge the practical application without collecting paired datasets in advance.
4 citations